probabilistic-thinking
Probabilistic & Bayesian Thinking
Core principle: Most real decisions happen under uncertainty. Probabilistic thinking replaces vague confidence with calibrated estimates. Bayesian thinking adds the discipline of updating those estimates as new evidence arrives — neither clinging to prior beliefs nor overreacting to new data.
Core Concepts
Probability as Degree of Belief
Probability isn't just for coin flips. It's a measure of how confident we are in any claim, given current evidence.
- "This will probably work" → What probability? 60%? 90%? The difference matters.
- Forcing a number exposes vague confidence and creates a baseline for updating.
Base Rates
Before estimating the probability of a specific event, find the base rate — how often does this type of event occur in a reference class?
"Will this feature succeed?" → What % of similar features in similar products succeeded?
More from andurilcode/craftwork
deep-document-processor
>
4summarizer
Apply this skill whenever the user asks to summarize, condense, distill, or compress any content — a document, article, meeting notes, conversation, codebase, book, research paper, video transcript, or any other source material. Triggers on phrases like 'summarize this', 'give me the TL;DR', 'condense this', 'what are the key points?', 'distill this down', 'brief me on this', 'what's the gist?', 'BLUF this', 'executive summary', 'compress this for me', or any request to reduce content while preserving its essential value. Also trigger when the user pastes a long text and implicitly wants it shortened, when they share a link and ask 'what does this say?', or when they ask for meeting notes or action items from a transcript. This skill does NOT apply to 'explain X to me' (use topic-explainer) or 'write a summary section for my doc' (use technical-writing). This skill is for when source material exists and needs to be compressed.
3inversion-premortem
Apply inversion and pre-mortem thinking whenever the user asks to evaluate a plan, strategy, architecture, feature, or decision before execution — or when they want to stress-test something that already exists. Triggers on phrases like "is this a good idea?", "what could go wrong?", "review this plan", "should we do this?", "are we missing anything?", "stress-test this", "what are the risks?", or any request to validate a decision or design. Use this skill proactively — if the user is about to commit to something, this skill should be consulted even if they don't ask for it explicitly.
3llms-txt-generator
Generate llms.txt-style context documents — token-budgeted, section-per-concept Markdown optimized for LLM and RAG consumption. Use this skill whenever someone asks to generate an llms.txt, create LLM-friendly documentation, produce a context document for a library or codebase, build a RAG-ready reference, make docs 'agent-readable', create a developer quick-reference, or says anything like 'generate context for X', 'make an llms.txt for this repo', 'create a reference doc for NotebookLM', 'turn these docs into something an LLM can use', 'context document', 'developer cheatsheet from docs'. Also trigger when someone provides a GitHub repo URL and asks for documentation synthesis, or when working inside a codebase and asked to produce a self-contained reference of how it works. This is the context engineer's doc generation tool — it turns sprawling documentation into precise, structured, token-efficient context.
3context-compressor
>
3context-cartography
Use when designing what goes into an agent's context window — system prompts, tool definitions, retrieval results, or any context artifact assembled before the agent runs. Triggers on "what should I put in the system prompt?", "how do I structure my context?", "the agent loses track of...", "my context window is full", "how do I decide what to include?", "designing a new harness", "the agent ignores my instructions". Do NOT use for one-off prompts, runtime conversation management, or when the problem is model capability rather than context design.
3